Noise reduction in low-dose ct using a 3D multiscale sparse denoising autoencoder

This article presents a novel neural network-based approach for enhancement of 3D medical image data. The proposed networks learn a sparse representation basis by mapping the corrupted input data to corresponding optimal targets. To reinforce the adjustment of the network to the given data, the threshold values are also adaptively learned. In order to capture important image features on various scales and be able to process large computed tomography (CT) volumes in a reasonable time, a multiscale approach is applied. Recursively downsampled versions of the input are used and denoising operator of constant size are learnt at each scale. The networks are trained end-to-end from a database of real highdose acquisitions with synthetic additional noise to simulate the corresponding low-dose scans. Both 2D and 3D networks are evaluated on CT volumes and compared to the block-matching and 3D filtering (BM3D) algorithm. The presented methods achieve an increase of 4% to 11% in the SSIM and of 2.4 to 2.8 dB in the PSNR with respect to the ground truth, outperform BM3D in quantitative comparisions and present no visible texture artifacts. By exploiting volumetric information, 3D networks achieve superior results over 2D networks.